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121
BUC algorithm for iceberg cubes
Published 2003“…The Uniform distribution is used as a basis for comparison. Results show that when the cube is sparse there is a correlation between the data distribution and the running time of the algorithm. …”
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conferenceObject -
122
An enhanced quorum selection algorithm
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123
An Improved Quorum Selection Algorithm
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conferenceObject -
124
Ensemble-Based Spam Detection in Smart Home IoT Devices Time Series Data Using Machine Learning Techniques
Published 2020“…The obtained results illustrate the efficacy of the proposed algorithm to analyze the time series data from the IoT devices for spam detection.…”
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The use of multi-task learning in cybersecurity applications: a systematic literature review
Published 2024“…Five critical applications, such as network intrusion detection and malware detection, were identified, and several tasks used in these applications were observed. Most of the studies used supervised learning algorithms, and there were very limited studies that focused on other types of machine learning. …”
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127
A hybrid heuristic approach to optimize rule based software quality estimation models. (c2008)
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128
A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48
Published 2022“…The performance of the algorithms for accuracy was evaluated using the Healthcare (heart attack possibility) dataset, freely available on kagle. …”
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129
Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT
Published 2021“…This paper proposes an Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT. …”
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130
QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning
Published 2024“…We collected PPG signals, demographic information, and blood pressure data from 139 diabetic (49.65%) and non-diabetic (50.35%) subjects. …”
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131
Calibration of building model based on indoor temperature for overheating assessment using genetic algorithm: Methodology, evaluation criteria, and case study
Published 2022“…It was found that the calibrated model achieved these metrics with RMSE of 0.3 ◦C, and MAD of 0.8 ◦C, and 85% of data points with an error less than 0.5 ◦C for a school building case.…”
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132
A Novel Genetic Algorithm Optimized Adversarial Attack in Federated Learning for Android-Based Mobile Systems
Published 2025“…<p dir="ltr">Federated Learning (FL) is gaining traction in Android-based consumer electronics, enabling collaborative model training across decentralized devices while preserving data privacy. However, the increasing adoption of FL in these devices exposes them to adversarial attacks that can compromise user data and device security. …”
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133
Stochastic Search Algorithms for Exam Scheduling
Published 2007“…Then, we empirically compare the three proposed algorithms and FESP using realistic data. Our experimental results show that SA and GA produce good exam schedules that are better than those of FESP heuristic procedure. …”
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134
Online Recruitment Fraud (ORF) Detection Using Deep Learning Approaches
Published 2024“…In recent studies, traditional machine learning and deep learning algorithms have been implemented to detect fake job postings; this research aims to use two transformer-based deep learning models, i.e., Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT-Pretraining Approach (RoBERTa) to detect fake job postings precisely. …”
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136
Bee Colony Algorithm for Proctors Assignment.
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137
Data Redundancy Management in Connected Environments
Published 2020“…., building) equipped with sensors that produce and exchange raw data. Although the sensed data is considered to contain useful and valuable information, yet it might include various inconsistencies such as data redundancies, anomalies, and missing values. …”
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conferenceObject -
138
Unsupervised outlier detection in multidimensional data
Published 2022“…<p>Detection and removal of outliers in a dataset is a fundamental preprocessing task without which the analysis of the data can be misleading. Furthermore, the existence of anomalies in the data can heavily degrade the performance of machine learning algorithms. …”
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Oversampling techniques for imbalanced data in regression
Published 2024“…For tabular data we conducted a comprehensive experiment using various models trained on both augmented and non-augmented datasets, followed by performance comparisons on test data. …”